{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95fb7ed7-85fb-4508-87ec-0e4d600ffc40",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math, random, os\n",
    "from typing import Dict, List\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "import xgboost as xgb\n",
    "import shap\n",
    "from xgboost.callback import EarlyStopping\n",
    "\n",
    "SEED = 42\n",
    "np.random.seed(SEED); random.seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d67ae353-146b-4b56-8c54-b1df4c55dad1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (237, 17) y shape: (237, 1)\n"
     ]
    }
   ],
   "source": [
    "# 读取数据\n",
    "exp_file = \"Kraken monophosphine coordinates AD Descriptors.xlsx\"\n",
    "exp_sheet = \"AD reduced\"                                \n",
    "df = pd.read_excel(exp_file,exp_sheet, header = 0)\n",
    "df = df.iloc[:,14:]\n",
    "\n",
    "# 指定特征列\n",
    "feature_cols = [c for c in df.columns if c != 'vbur_ratio_vbur_vtot']\n",
    "X = df[feature_cols].values.astype(np.float32)\n",
    "y = df['vbur_ratio_vbur_vtot'].values.astype(np.float32).reshape(-1, 1)\n",
    "\n",
    "print(\"X shape:\", X.shape, \"y shape:\", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ad4ce08e-c64e-41d1-82fa-31e55dd8a65d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Fold 1] MAE=0.017 RMSE=0.024 R2=0.761  BestIter=660\n",
      "[Fold 2] MAE=0.019 RMSE=0.030 R2=0.759  BestIter=432\n",
      "[Fold 3] MAE=0.015 RMSE=0.019 R2=0.795  BestIter=338\n",
      "[Fold 4] MAE=0.013 RMSE=0.017 R2=0.827  BestIter=444\n",
      "[Fold 5] MAE=0.023 RMSE=0.031 R2=0.747  BestIter=465\n"
     ]
    }
   ],
   "source": [
    "# # 2. 5-fold CV: train XGBoost, record metrics, compute SHAP per fold\n",
    "# - We compute TreeSHAP on the validation set of each fold\n",
    "# - Aggregate mean(|SHAP|) per feature in each fold\n",
    "# - Later we will combine (mean/std/top-k frequency) across folds\n",
    "\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=SEED)\n",
    "\n",
    "fold_metrics = []                 # (MAE, RMSE, R2)\n",
    "fold_importances = []             # pd.Series of mean(|SHAP|) per feature (per fold)\n",
    "models_for_plot = None            # keep one fold’s model & val set for plots\n",
    "models = []\n",
    "\n",
    "for fold, (tr_idx, va_idx) in enumerate(kf.split(X), 1):\n",
    "    X_tr, y_tr = X[tr_idx], y[tr_idx]\n",
    "    X_va, y_va = X[va_idx], y[va_idx]\n",
    "\n",
    "    # XGBoost Regressor with early stopping\n",
    "    model = xgb.XGBRegressor(\n",
    "        n_estimators=2000,\n",
    "        learning_rate=0.03,\n",
    "        max_depth=4,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        reg_lambda=2.0,\n",
    "        random_state=SEED,\n",
    "        eval_metric=\"rmse\",\n",
    "        callbacks=[EarlyStopping(rounds=100, save_best=True)]\n",
    "    )\n",
    "    model.fit(X_tr, y_tr, eval_set=[(X_va, y_va)], verbose=False)\n",
    "\n",
    "    # Metrics\n",
    "    y_pred = model.predict(X_va).reshape(-1,1)\n",
    "    mae  = mean_absolute_error(y_va, y_pred)\n",
    "    rmse = math.sqrt(((y_va - y_pred)**2).mean())\n",
    "    # r2\n",
    "    ss_res = np.sum((y_va - y_pred)**2)\n",
    "    ss_tot = np.sum((y_va - y_va.mean())**2)\n",
    "    r2 = 1 - ss_res/ss_tot if ss_tot > 0 else float(\"nan\")\n",
    "    fold_metrics.append((mae, rmse, r2))\n",
    "    models.append(model)\n",
    "    print(f\"[Fold {fold}] MAE={mae:.3f} RMSE={rmse:.3f} R2={r2:.3f}  BestIter={model.best_iteration}\")\n",
    "\n",
    "    # SHAP on validation set\n",
    "    explainer = shap.TreeExplainer(model)\n",
    "    shap_vals = explainer.shap_values(X_va)           # shape: (N_val, 18)\n",
    "    shap_abs  = np.abs(shap_vals)\n",
    "\n",
    "    # mean(|SHAP|) per feature\n",
    "    imp = shap_abs.mean(axis=0)                       # length 18\n",
    "    imp_series = pd.Series(imp, index=feature_cols).sort_values(ascending=False)\n",
    "    fold_importances.append(imp_series)\n",
    "\n",
    "    # save one fold for plotting later\n",
    "    if models_for_plot is None:\n",
    "        models_for_plot = (model, X_va, y_va, feature_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "822b5403-22c7-4002-8712-b2b7ee0052dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV Performance (mean ± std)\n",
      "MAE : 0.017 ± 0.003\n",
      "RMSE: 0.024 ± 0.006\n",
      "R^2 : 0.778 ± 0.029\n"
     ]
    }
   ],
   "source": [
    "# # 3. CV Performance Summary\n",
    "\n",
    "mae_arr  = np.array([m[0] for m in fold_metrics])\n",
    "rmse_arr = np.array([m[1] for m in fold_metrics])\n",
    "r2_arr   = np.array([m[2] for m in fold_metrics])\n",
    "\n",
    "print(\"CV Performance (mean ± std)\")\n",
    "print(f\"MAE : {mae_arr.mean():.3f} ± {mae_arr.std():.3f}\")\n",
    "print(f\"RMSE: {rmse_arr.mean():.3f} ± {rmse_arr.std():.3f}\")\n",
    "print(f\"R^2 : {r2_arr.mean():.3f} ± {r2_arr.std():.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f3c03925-b2bd-4a27-8f6d-c4c837ec1200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>mean_importance</th>\n",
       "      <th>std_importance</th>\n",
       "      <th>topk_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E_solv_total</td>\n",
       "      <td>0.012634</td>\n",
       "      <td>0.001552</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sterimol_B1</td>\n",
       "      <td>0.010927</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vbur_vbur.2</td>\n",
       "      <td>0.008725</td>\n",
       "      <td>0.001380</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sterimol_B5</td>\n",
       "      <td>0.005643</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vbur_vbur.3</td>\n",
       "      <td>0.005069</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fmo_eta</td>\n",
       "      <td>0.004082</td>\n",
       "      <td>0.001753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sterimol_burL</td>\n",
       "      <td>0.003764</td>\n",
       "      <td>0.001054</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sterimol_L</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.000669</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sterimol_burB1</td>\n",
       "      <td>0.003230</td>\n",
       "      <td>0.001810</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>vmin_vmin</td>\n",
       "      <td>0.003066</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>nbo_bd_e_max</td>\n",
       "      <td>0.002634</td>\n",
       "      <td>0.000983</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>fmo_mu</td>\n",
       "      <td>0.002410</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>nbo_P_ra</td>\n",
       "      <td>0.001628</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nmr_P</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>vbur_vtot.2</td>\n",
       "      <td>0.001046</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nbo_lp_P_percent_s</td>\n",
       "      <td>0.000775</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sterimol_burB5</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature  mean_importance  std_importance  topk_frequency\n",
       "0         E_solv_total         0.012634        0.001552               5\n",
       "1          sterimol_B1         0.010927        0.000969               5\n",
       "2          vbur_vbur.2         0.008725        0.001380               5\n",
       "3          sterimol_B5         0.005643        0.000227               4\n",
       "4          vbur_vbur.3         0.005069        0.000537               2\n",
       "5              fmo_eta         0.004082        0.001753               2\n",
       "6        sterimol_burL         0.003764        0.001054               1\n",
       "7           sterimol_L         0.003383        0.000669               0\n",
       "8       sterimol_burB1         0.003230        0.001810               1\n",
       "9            vmin_vmin         0.003066        0.000736               0\n",
       "10        nbo_bd_e_max         0.002634        0.000983               0\n",
       "11              fmo_mu         0.002410        0.000867               0\n",
       "12            nbo_P_ra         0.001628        0.000541               0\n",
       "13               nmr_P         0.001249        0.000782               0\n",
       "14         vbur_vtot.2         0.001046        0.000461               0\n",
       "15  nbo_lp_P_percent_s         0.000775        0.000367               0\n",
       "16      sterimol_burB5         0.000642        0.000188               0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 4. Consistency Screening: mean/std across folds + Top-k frequency\n",
    "# - For each feature, compute:\n",
    "#     - mean_importance (mean of mean(|SHAP|) across folds)\n",
    "#     - std_importance\n",
    "#     - topk_frequency: number of folds where the feature is in Top-k\n",
    "# - Sort by mean_importance desc, export CSV\n",
    "\n",
    "TOP_K = 5\n",
    "\n",
    "# unify feature set\n",
    "all_feats = feature_cols[:]\n",
    "agg_rows = []\n",
    "for f in all_feats:\n",
    "    vals = np.array([s.get(f, 0.0) for s in fold_importances])  # importance per fold for feature f\n",
    "    # rank per fold (1=most important)\n",
    "    ranks = np.array([s.rank(ascending=False, method=\"dense\").loc[f] for s in fold_importances])\n",
    "    topk_hits = int(np.sum(ranks <= TOP_K))\n",
    "    agg_rows.append({\n",
    "        \"feature\": f,\n",
    "        \"mean_importance\": float(vals.mean()),\n",
    "        \"std_importance\": float(vals.std()),\n",
    "        \"topk_frequency\": topk_hits\n",
    "    })\n",
    "\n",
    "consensus_df = pd.DataFrame(agg_rows).sort_values(\"mean_importance\", ascending=False).reset_index(drop=True)\n",
    "consensus_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ac896fc-3944-4e7a-a382-a13be7197918",
   "metadata": {},
   "outputs": [
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nu(t,e.child,null,n),(e=Hu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function qu(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),_a(e.return,t,n)}function Qu(e,t,n,r,a){var i=e.memoizedState;null===i?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:a}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=a)}function Yu(e,t,n){var r=t.pendingProps,a=r.revealOrder,i=r.tail;if(Pu(e,t,r.children,n),2&(r=cu.current))r=1&r|2,t.flags|=128;else{if(null!==e&&128&e.flags)e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&qu(e,n,t);else if(19===e.tag)qu(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}switch($(cu,r),a){case\"forwards\":for(n=t.child,a=null;null!==n;)null!==(e=n.alternate)&&null===fu(e)&&(a=n),n=n.sibling;null===(n=a)?(a=t.child,t.child=null):(a=n.sibling,n.sibling=null),Qu(t,!1,a,n,i);break;case\"backwards\":for(n=null,a=t.child,t.child=null;null!==a;){if(null!==(e=a.alternate)&&null===fu(e)){t.child=a;break}e=a.sibling,a.sibling=n,n=a,a=e}Qu(t,!0,n,null,i);break;case\"together\":Qu(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Gu(e,t,n){if(null!==e&&(t.dependencies=e.dependencies),ps|=t.lanes,!(n&t.childLanes)){if(null===e)return null;if(xa(e,t,n,!1),!(n&t.childLanes))return null}if(null!==e&&t.child!==e.child)throw Error(o(153));if(null!==t.child){for(n=jr(e=t.child,e.pendingProps),t.child=n,n.return=t;null!==e.sibling;)e=e.sibling,(n=n.sibling=jr(e,e.pendingProps)).return=t;n.sibling=null}return t.child}function Ku(e,t){return!!(e.lanes&t)||!(null===(e=e.dependencies)||!Sa(e))}function Xu(e,t,n){if(null!==e)if(e.memoizedProps!==t.pendingProps)Tu=!0;else{if(!(Ku(e,n)||128&t.flags))return Tu=!1,function(e,t,n){switch(t.tag){case 3:q(t,t.stateNode.containerInfo),ba(0,Aa,e.memoizedState.cache),pa();break;case 27:case 5:Y(t);break;case 4:q(t,t.stateNode.containerInfo);break;case 10:ba(0,t.type,t.memoizedProps.value);break;case 13:var r=t.memoizedState;if(null!==r)return null!==r.dehydrated?(ou(t),t.flags|=128,null):n&t.child.childLanes?Bu(e,t,n):(ou(t),null!==(e=Gu(e,t,n))?e.sibling:null);ou(t);break;case 19:var a=!!(128&e.flags);if((r=!!(n&t.childLanes))||(xa(e,t,n,!1),r=!!(n&t.childLanes)),a){if(r)return Yu(e,t,n);t.flags|=128}if(null!==(a=t.memoizedState)&&(a.rendering=null,a.tail=null,a.lastEffect=null),$(cu,cu.current),r)break;return null;case 22:case 23:return t.lanes=0,Au(e,t,n);case 24:ba(0,Aa,e.memoizedState.cache)}return Gu(e,t,n)}(e,t,n);Tu=!!(131072&e.flags)}else Tu=!1,ia&&1048576&t.flags&&ea(t,Qr,t.index);switch(t.lanes=0,t.tag){case 16:e:{e=t.pendingProps;var r=t.elementType,a=r._init;if(r=a(r._payload),t.type=r,\"function\"!=typeof r){if(null!=r){if((a=r.$$typeof)===k){t.tag=11,t=zu(null,t,r,e,n);break e}if(a===E){t.tag=14,t=Mu(null,t,r,e,n);break e}}throw t=A(r)||r,Error(o(306,t,\"\"))}Rr(r)?(e=vu(r,e),t.tag=1,t=Ru(null,t,r,e,n)):(t.tag=0,t=Fu(null,t,r,e,n))}return t;case 0:return Fu(e,t,t.type,t.pendingProps,n);case 1:return Ru(e,t,r=t.type,a=vu(r,t.pendingProps),n);case 3:e:{if(q(t,t.stateNode.containerInfo),null===e)throw Error(o(387));r=t.pendingProps;var i=t.memoizedState;a=i.element,ri(e,t),ci(t,r,null,n);var u=t.memoizedState;if(r=u.cache,ba(0,Aa,r),r!==i.cache&&ka(t,[Aa],n,!0),si(),r=u.element,i.isDehydrated){if(i={element:r,isDehydrated:!1,cache:u.cache},t.updateQueue.baseState=i,t.memoizedState=i,256&t.flags){t=ju(e,t,r,n);break e}if(r!==a){ga(a=Sr(Error(o(424)),t)),t=ju(e,t,r,n);break e}for(e=9===(e=t.stateNode.containerInfo).nodeType?e.body:\"HTML\"===e.nodeName?e.ownerDocument.body:e,aa=yf(e.firstChild),ra=t,ia=!0,oa=null,ua=!0,n=ru(t,null,r,n),t.child=n;n;)n.flags=-3&n.flags|4096,n=n.sibling}else{if(pa(),r===a){t=Gu(e,t,n);break e}Pu(e,t,r,n)}t=t.child}return t;case 26:return Lu(e,t),null===e?(n=zf(t.type,null,t.pendingProps,null))?t.memoizedState=n:ia||(n=t.type,e=t.pendingProps,(r=rf(W.current).createElement(n))[Ae]=t,r[Oe]=e,ef(r,n,e),Ve(r),t.stateNode=r):t.memoizedState=zf(t.type,e.memoizedProps,t.pendingProps,e.memoizedState),null;case 27:return Y(t),null===e&&ia&&(r=t.stateNode=_f(t.type,t.pendingProps,W.current),ra=t,ua=!0,a=aa,hf(t.type)?(bf=a,aa=yf(r.firstChild)):aa=a),Pu(e,t,t.pendingProps.children,n),Lu(e,t),null===e&&(t.flags|=4194304),t.child;case 5:return null===e&&ia&&((a=r=aa)&&(null!==(r=function(e,t,n,r){for(;1===e.nodeType;){var a=n;if(e.nodeName.toLowerCase()!==t.toLowerCase()){if(!r&&(\"INPUT\"!==e.nodeName||\"hidden\"!==e.type))break}else if(r){if(!e[Ue])switch(t){case\"meta\":if(!e.hasAttribute(\"itemprop\"))break;return e;case\"link\":if(\"stylesheet\"===(i=e.getAttribute(\"rel\"))&&e.hasAttribute(\"data-precedence\"))break;if(i!==a.rel||e.getAttribute(\"href\")!==(null==a.href||\"\"===a.href?null:a.href)||e.getAttribute(\"crossorigin\")!==(null==a.crossOrigin?null:a.crossOrigin)||e.getAttribute(\"title\")!==(null==a.title?null:a.title))break;return e;case\"style\":if(e.hasAttribute(\"data-precedence\"))break;return e;case\"script\":if(((i=e.getAttribute(\"src\"))!==(null==a.src?null:a.src)||e.getAttribute(\"type\")!==(null==a.type?null:a.type)||e.getAttribute(\"crossorigin\")!==(null==a.crossOrigin?null:a.crossOrigin))&&i&&e.hasAttribute(\"async\")&&!e.hasAttribute(\"itemprop\"))break;return e;default:return e}}else{if(\"input\"!==t||\"hidden\"!==e.type)return e;var i=null==a.name?null:\"\"+a.name;if(\"hidden\"===a.type&&e.getAttribute(\"name\")===i)return e}if(null===(e=yf(e.nextSibling)))break}return null}(r,t.type,t.pendingProps,ua))?(t.stateNode=r,ra=t,aa=yf(r.firstChild),ua=!1,a=!0):a=!1),a||sa(t)),Y(t),a=t.type,i=t.pendingProps,u=null!==e?e.memoizedProps:null,r=i.children,uf(a,i)?r=null:null!==u&&uf(a,u)&&(t.flags|=32),null!==t.memoizedState&&(a=Ni(e,t,Li,null,null,n),Gf._currentValue=a),Lu(e,t),Pu(e,t,r,n),t.child;case 6:return null===e&&ia&&((e=n=aa)&&(null!==(n=function(e,t,n){if(\"\"===t)return null;for(;3!==e.nodeType;){if((1!==e.nodeType||\"INPUT\"!==e.nodeName||\"hidden\"!==e.type)&&!n)return null;if(null===(e=yf(e.nextSibling)))return null}return e}(n,t.pendingProps,ua))?(t.stateNode=n,ra=t,aa=null,e=!0):e=!1),e||sa(t)),null;case 13:return Bu(e,t,n);case 4:return q(t,t.stateNode.containerInfo),r=t.pendingProps,null===e?t.child=nu(t,null,r,n):Pu(e,t,r,n),t.child;case 11:return zu(e,t,t.type,t.pendingProps,n);case 7:return Pu(e,t,t.pendingProps,n),t.child;case 8:case 12:return Pu(e,t,t.pendingProps.children,n),t.child;case 10:return r=t.pendingProps,ba(0,t.type,r.value),Pu(e,t,r.children,n),t.child;case 9:return a=t.type._context,r=t.pendingProps.children,Ea(t),r=r(a=Ca(a)),t.flags|=1,Pu(e,t,r,n),t.child;case 14:return Mu(e,t,t.type,t.pendingProps,n);case 15:return Nu(e,t,t.type,t.pendingProps,n);case 19:return Yu(e,t,n);case 31:return r=t.pendingProps,n=t.mode,r={mode:r.mode,children:r.children},null===e?((n=Wu(r,n)).ref=t.ref,t.child=n,n.return=t,t=n):((n=jr(e.child,r)).ref=t.ref,t.child=n,n.return=t,t=n),t;case 22:return Au(e,t,n);case 24:return Ea(t),r=Ca(Aa),null===e?(null===(a=Ba())&&(a=rs,i=Oa(),a.pooledCache=i,i.refCount++,null!==i&&(a.pooledCacheLanes|=n),a=i),t.memoizedState={parent:r,cache:a},ni(t),ba(0,Aa,a)):(!!(e.lanes&n)&&(ri(e,t),ci(t,null,null,n),si()),a=e.memoizedState,i=t.memoizedState,a.parent!==r?(a={parent:r,cache:r},t.memoizedState=a,0===t.lanes&&(t.memoizedState=t.updateQueue.baseState=a),ba(0,Aa,r)):(r=i.cache,ba(0,Aa,r),r!==a.cache&&ka(t,[Aa],n,!0))),Pu(e,t,t.pendingProps.children,n),t.child;case 29:throw t.pendingProps}throw Error(o(156,t.tag))}function Zu(e){e.flags|=4}function Ju(e,t){if(\"stylesheet\"!==t.type||4&t.state.loading)e.flags&=-16777217;else if(e.flags|=16777216,!Bf(t)){if(null!==(t=au.current)&&((4194048&is)===is?null!==iu:(62914560&is)!==is&&!(536870912&is)||t!==iu))throw Za=Ya,qa;e.flags|=8192}}function el(e,t){null!==t&&(e.flags|=4),16384&e.flags&&(t=22!==e.tag?xe():536870912,e.lanes|=t,ms|=t)}function tl(e,t){if(!ia)switch(e.tailMode){case\"hidden\":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case\"collapsed\":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function nl(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=65011712&a.subtreeFlags,r|=65011712&a.flags,a.return=e,a=a.sibling;else for(a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function rl(e,t,n){var r=t.pendingProps;switch(na(t),t.tag){case 31:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:case 1:return nl(t),null;case 3:return n=t.stateNode,r=null,null!==e&&(r=e.memoizedState.cache),t.memoizedState.cache!==r&&(t.flags|=2048),wa(Aa),Q(),n.pendingContext&&(n.context=n.pendingContext,n.pendingContext=null),null!==e&&null!==e.child||(da(t)?Zu(t):null===e||e.memoizedState.isDehydrated&&!(256&t.flags)||(t.flags|=1024,ha())),nl(t),null;case 26:return n=t.memoizedState,null===e?(Zu(t),null!==n?(nl(t),Ju(t,n)):(nl(t),t.flags&=-16777217)):n?n!==e.memoizedState?(Zu(t),nl(t),Ju(t,n)):(nl(t),t.flags&=-16777217):(e.memoizedProps!==r&&Zu(t),nl(t),t.flags&=-16777217),null;case 27:G(t),n=W.current;var a=t.type;if(null!==e&&null!=t.stateNode)e.memoizedProps!==r&&Zu(t);else{if(!r){if(null===t.stateNode)throw Error(o(166));return nl(t),null}e=B.current,da(t)?ca(t):(e=_f(a,r,n),t.stateNode=e,Zu(t))}return nl(t),null;case 5:if(G(t),n=t.type,null!==e&&null!=t.stateNode)e.memoizedProps!==r&&Zu(t);else{if(!r){if(null===t.stateNode)throw Error(o(166));return nl(t),null}if(e=B.current,da(t))ca(t);else{switch(a=rf(W.current),e){case 1:e=a.createElementNS(\"http://www.w3.org/2000/svg\",n);break;case 2:e=a.createElementNS(\"http://www.w3.org/1998/Math/MathML\",n);break;default:switch(n){case\"svg\":e=a.createElementNS(\"http://www.w3.org/2000/svg\",n);break;case\"math\":e=a.createElementNS(\"http://www.w3.org/1998/Math/MathML\",n);break;case\"script\":(e=a.createElement(\"div\")).innerHTML=\"<script><\\/script>\",e=e.removeChild(e.firstChild);break;case\"select\":e=\"string\"==typeof r.is?a.createElement(\"select\",{is:r.is}):a.createElement(\"select\"),r.multiple?e.multiple=!0:r.size&&(e.size=r.size);break;default:e=\"string\"==typeof r.is?a.createElement(n,{is:r.is}):a.createElement(n)}}e[Ae]=t,e[Oe]=r;e:for(a=t.child;null!==a;){if(5===a.tag||6===a.tag)e.appendChild(a.stateNode);else if(4!==a.tag&&27!==a.tag&&null!==a.child){a.child.return=a,a=a.child;continue}if(a===t)break e;for(;null===a.sibling;){if(null===a.return||a.return===t)break e;a=a.return}a.sibling.return=a.return,a=a.sibling}t.stateNode=e;e:switch(ef(e,n,r),n){case\"button\":case\"input\":case\"select\":case\"textarea\":e=!!r.autoFocus;break e;case\"img\":e=!0;break e;default:e=!1}e&&Zu(t)}}return nl(t),t.flags&=-16777217,null;case 6:if(e&&null!=t.stateNode)e.memoizedProps!==r&&Zu(t);else{if(\"string\"!=typeof r&&null===t.stateNode)throw Error(o(166));if(e=W.current,da(t)){if(e=t.stateNode,n=t.memoizedProps,r=null,null!==(a=ra))switch(a.tag){case 27:case 5:r=a.memoizedProps}e[Ae]=t,(e=!!(e.nodeValue===n||null!==r&&!0===r.suppressHydrationWarning||Kc(e.nodeValue,n)))||sa(t)}else(e=rf(e).createTextNode(r))[Ae]=t,t.stateNode=e}return nl(t),null;case 13:if(r=t.memoizedState,null===e||null!==e.memoizedState&&null!==e.memoizedState.dehydrated){if(a=da(t),null!==r&&null!==r.dehydrated){if(null===e){if(!a)throw Error(o(318));if(!(a=null!==(a=t.memoizedState)?a.dehydrated:null))throw Error(o(317));a[Ae]=t}else pa(),!(128&t.flags)&&(t.memoizedState=null),t.flags|=4;nl(t),a=!1}else a=ha(),null!==e&&null!==e.memoizedState&&(e.memoizedState.hydrationErrors=a),a=!0;if(!a)return 256&t.flags?(su(t),t):(su(t),null)}if(su(t),128&t.flags)return t.lanes=n,t;if(n=null!==r,e=null!==e&&null!==e.memoizedState,n){a=null,null!==(r=t.child).alternate&&null!==r.alternate.memoizedState&&null!==r.alternate.memoizedState.cachePool&&(a=r.alternate.memoizedState.cachePool.pool);var i=null;null!==r.memoizedState&&null!==r.memoizedState.cachePool&&(i=r.memoizedState.cachePool.pool),i!==a&&(r.flags|=2048)}return n!==e&&n&&(t.child.flags|=8192),el(t,t.updateQueue),nl(t),null;case 4:return Q(),null===e&&Ic(t.stateNode.containerInfo),nl(t),null;case 10:return wa(t.type),nl(t),null;case 19:if(I(cu),null===(a=t.memoizedState))return 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      ],
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\litao0056\\AppData\\Local\\Temp\\ipykernel_29900\\1985524267.py:11: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
      "  shap.summary_plot(shap_vals_plot, features=X_val_plot, feature_names=feat_names, show=True)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x830 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-3 features for dependence plot: ['E_solv_total', 'sterimol_B1', 'vbur_vbur.2']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # 6. Basic SHAP Visualizations (using the saved fold)\n",
    "# - Global summary plot\n",
    "# - Dependence plots for top continuous features (from consensus ranking)\n",
    "\n",
    "shap.initjs()\n",
    "model, X_val_plot, y_val_plot, feat_names = models_for_plot\n",
    "explainer = shap.TreeExplainer(model)\n",
    "shap_vals_plot = explainer.shap_values(X_val_plot)\n",
    "\n",
    "# 1) Global summary\n",
    "shap.summary_plot(shap_vals_plot, features=X_val_plot, feature_names=feat_names, show=True)\n",
    "\n",
    "# 2) Dependence plots for top-3 features\n",
    "top3 = consensus_df[\"feature\"].tolist()[:3]\n",
    "print(\"Top-3 features for dependence plot:\", top3)\n",
    "for f in top3:\n",
    "    shap.dependence_plot(f, shap_vals_plot, X_val_plot, feature_names=feat_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fde134e0-16ff-4066-9cb7-2a83c5995422",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example mean/std: [0.18817325 0.21661536 0.22021973 0.19848385 0.21542296] [0.00092155 0.00460495 0.00136494 0.0061078  0.00314933]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idx</th>\n",
       "      <th>pred_mean</th>\n",
       "      <th>pred_std</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>227</td>\n",
       "      <td>0.348908</td>\n",
       "      <td>0.053366</td>\n",
       "      <td>0.945303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>0.381783</td>\n",
       "      <td>0.036371</td>\n",
       "      <td>0.839990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75</td>\n",
       "      <td>0.323613</td>\n",
       "      <td>0.026590</td>\n",
       "      <td>0.651119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>233</td>\n",
       "      <td>0.340037</td>\n",
       "      <td>0.015985</td>\n",
       "      <td>0.578598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>187</td>\n",
       "      <td>0.264829</td>\n",
       "      <td>0.023889</td>\n",
       "      <td>0.527891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "      <td>0.320493</td>\n",
       "      <td>0.012707</td>\n",
       "      <td>0.515226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>115</td>\n",
       "      <td>0.269321</td>\n",
       "      <td>0.021387</td>\n",
       "      <td>0.511805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>234</td>\n",
       "      <td>0.308169</td>\n",
       "      <td>0.013303</td>\n",
       "      <td>0.500326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>222</td>\n",
       "      <td>0.316088</td>\n",
       "      <td>0.009087</td>\n",
       "      <td>0.473809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>125</td>\n",
       "      <td>0.265336</td>\n",
       "      <td>0.016958</td>\n",
       "      <td>0.463474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>132</td>\n",
       "      <td>0.264847</td>\n",
       "      <td>0.015777</td>\n",
       "      <td>0.451541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>203</td>\n",
       "      <td>0.171976</td>\n",
       "      <td>0.031842</td>\n",
       "      <td>0.448280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>103</td>\n",
       "      <td>0.266500</td>\n",
       "      <td>0.013048</td>\n",
       "      <td>0.428604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>232</td>\n",
       "      <td>0.332670</td>\n",
       "      <td>0.001217</td>\n",
       "      <td>0.427310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>48</td>\n",
       "      <td>0.253424</td>\n",
       "      <td>0.014410</td>\n",
       "      <td>0.419670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>170</td>\n",
       "      <td>0.293356</td>\n",
       "      <td>0.006548</td>\n",
       "      <td>0.412087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>166</td>\n",
       "      <td>0.275054</td>\n",
       "      <td>0.008931</td>\n",
       "      <td>0.404074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67</td>\n",
       "      <td>0.289123</td>\n",
       "      <td>0.006317</td>\n",
       "      <td>0.402865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>183</td>\n",
       "      <td>0.256025</td>\n",
       "      <td>0.011686</td>\n",
       "      <td>0.398350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>149</td>\n",
       "      <td>0.183351</td>\n",
       "      <td>0.024517</td>\n",
       "      <td>0.398235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>214</td>\n",
       "      <td>0.241110</td>\n",
       "      <td>0.013730</td>\n",
       "      <td>0.392775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>204</td>\n",
       "      <td>0.218876</td>\n",
       "      <td>0.016976</td>\n",
       "      <td>0.386349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>202</td>\n",
       "      <td>0.236465</td>\n",
       "      <td>0.013451</td>\n",
       "      <td>0.382422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>77</td>\n",
       "      <td>0.217562</td>\n",
       "      <td>0.016668</td>\n",
       "      <td>0.381262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>114</td>\n",
       "      <td>0.265326</td>\n",
       "      <td>0.008108</td>\n",
       "      <td>0.380133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>143</td>\n",
       "      <td>0.148935</td>\n",
       "      <td>0.028536</td>\n",
       "      <td>0.378819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>195</td>\n",
       "      <td>0.242215</td>\n",
       "      <td>0.011956</td>\n",
       "      <td>0.377911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>130</td>\n",
       "      <td>0.236039</td>\n",
       "      <td>0.012685</td>\n",
       "      <td>0.374502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>47</td>\n",
       "      <td>0.274057</td>\n",
       "      <td>0.005482</td>\n",
       "      <td>0.369940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>56</td>\n",
       "      <td>0.224198</td>\n",
       "      <td>0.014288</td>\n",
       "      <td>0.369898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    idx  pred_mean  pred_std     score\n",
       "0   227   0.348908  0.053366  0.945303\n",
       "1    20   0.381783  0.036371  0.839990\n",
       "2    75   0.323613  0.026590  0.651119\n",
       "3   233   0.340037  0.015985  0.578598\n",
       "4   187   0.264829  0.023889  0.527891\n",
       "5    19   0.320493  0.012707  0.515226\n",
       "6   115   0.269321  0.021387  0.511805\n",
       "7   234   0.308169  0.013303  0.500326\n",
       "8   222   0.316088  0.009087  0.473809\n",
       "9   125   0.265336  0.016958  0.463474\n",
       "10  132   0.264847  0.015777  0.451541\n",
       "11  203   0.171976  0.031842  0.448280\n",
       "12  103   0.266500  0.013048  0.428604\n",
       "13  232   0.332670  0.001217  0.427310\n",
       "14   48   0.253424  0.014410  0.419670\n",
       "15  170   0.293356  0.006548  0.412087\n",
       "16  166   0.275054  0.008931  0.404074\n",
       "17   67   0.289123  0.006317  0.402865\n",
       "18  183   0.256025  0.011686  0.398350\n",
       "19  149   0.183351  0.024517  0.398235\n",
       "20  214   0.241110  0.013730  0.392775\n",
       "21  204   0.218876  0.016976  0.386349\n",
       "22  202   0.236465  0.013451  0.382422\n",
       "23   77   0.217562  0.016668  0.381262\n",
       "24  114   0.265326  0.008108  0.380133\n",
       "25  143   0.148935  0.028536  0.378819\n",
       "26  195   0.242215  0.011956  0.377911\n",
       "27  130   0.236039  0.012685  0.374502\n",
       "28   47   0.274057  0.005482  0.369940\n",
       "29   56   0.224198  0.014288  0.369898"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每个模型对全量 X 预测\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=SEED)\n",
    "\n",
    "pred_matrix = np.stack([m.predict(X) for m in models], axis=0)   # shape: (K, N)\n",
    "mean_pred = pred_matrix.mean(axis=0)                             # (N,)\n",
    "std_pred  = pred_matrix.std(axis=0, ddof=0)                      # (N,)\n",
    "\n",
    "print(\"Example mean/std:\", mean_pred[:5], std_pred[:5])\n",
    "\n",
    "# ---- 5) Rank by combined score (same formula as your MC snippet) ----\n",
    "num_take = 30\n",
    "score = 0.5 * ((mean_pred - mean_pred.min()) / (mean_pred.ptp() + 1e-9)) + \\\n",
    "        0.5 * ((std_pred  - std_pred.min())  / (std_pred.ptp()  + 1e-9))\n",
    "rank = np.argsort(-score)   # 倒序\n",
    "suggest_top = rank[:num_take]\n",
    "\n",
    "result_df = pd.DataFrame({\n",
    "    \"idx\": suggest_top,\n",
    "    \"pred_mean\": mean_pred[suggest_top],\n",
    "    \"pred_std\": std_pred[suggest_top],\n",
    "    \"score\": score[suggest_top],\n",
    "})\n",
    "result_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43c56ac9-8b0b-4988-a40f-1e4b602d1fe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "143    0.131206\n",
      "203    0.152294\n",
      "149    0.166557\n",
      "77     0.206160\n",
      "56     0.214838\n",
      "204    0.228718\n",
      "130    0.242103\n",
      "202    0.245633\n",
      "195    0.248654\n",
      "214    0.250555\n",
      "48     0.261828\n",
      "183    0.264737\n",
      "114    0.270344\n",
      "132    0.273790\n",
      "125    0.274760\n",
      "103    0.275713\n",
      "47     0.278151\n",
      "187    0.279615\n",
      "166    0.281057\n",
      "67     0.286499\n",
      "115    0.286708\n",
      "170    0.296871\n",
      "234    0.300382\n",
      "222    0.310319\n",
      "19     0.314514\n",
      "232    0.332863\n",
      "75     0.338394\n",
      "233    0.348492\n",
      "227    0.382245\n",
      "20     0.405615\n",
      "Name: vbur_ratio_vbur_vtot, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "aa = df.iloc[suggest_top,-1].sort_values()\n",
    "print(aa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "24f53190-f3e1-40b8-8312-9c44bd9ba001",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200    0.253926\n",
      "6      0.257221\n",
      "28     0.260941\n",
      "48     0.261828\n",
      "139    0.262513\n",
      "51     0.263955\n",
      "183    0.264737\n",
      "10     0.268322\n",
      "46     0.269007\n",
      "114    0.270344\n",
      "132    0.273790\n",
      "125    0.274760\n",
      "26     0.275096\n",
      "103    0.275713\n",
      "47     0.278151\n",
      "231    0.278946\n",
      "187    0.279615\n",
      "12     0.281000\n",
      "166    0.281057\n",
      "67     0.286499\n",
      "115    0.286708\n",
      "170    0.296871\n",
      "234    0.300382\n",
      "222    0.310319\n",
      "19     0.314514\n",
      "232    0.332863\n",
      "75     0.338394\n",
      "233    0.348492\n",
      "227    0.382245\n",
      "20     0.405615\n",
      "Name: vbur_ratio_vbur_vtot, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df_sorted = df.sort_values(by='vbur_ratio_vbur_vtot')\n",
    "bb = df_sorted.iloc[:,-1][-num_take:]\n",
    "print (bb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cf873cf0-cd81-4ede-af12-d30175712b1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6666666666666667\n"
     ]
    }
   ],
   "source": [
    "print(1-len(set(aa) - set(bb))/num_take)"
   ]
  }
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